Vortragsreiheurn:uuid:914a5f31-3c85-3808-8c8e-e57f7bcde5432019-02-22T14:00:17.836591ZLektor Atom PluginIngrid Lossius Falkum – Count-mass polysemy: A pragmatic approachurn:uuid:75e61de3-6ef3-334d-9c65-28bb726c727f2019-02-22T14:00:17.723445Z<div class="rubbullets rublinks text">
<p>In this talk I discuss a subtype of polysemy which in English (and several other languages) appears to rest on the distinction between count and mass uses of nouns (e.g., <em>shoot a rabbit</em>/<em>eat rabbit</em>/<em>wear rabbit</em>). I question the widespread assumption that such meaning alternations are mainly linguistically generated and argue, based on the variety of interpretations that the alternation between count and mass uses of nouns may give rise to, that the linguistic component provided by count-mass syntax leaves a more underspecified semantic output, subject to pragmatic modulation in context, than what some semantic theories tend to assume.</p>
</div>Kilian Evang – Exploring transition-based methods for semantic parsing to Discourse Representation Structuresurn:uuid:e5f279ad-f0b2-38a2-a7cf-448792fd66a42019-02-22T14:00:17.727086Z<div class="rubbullets rublinks text">
<p>Der Abstract für den Vortrag wird zwei bis drei Wochen vor dem Termin bekanntgegeben.</p>
</div>Roberto Zamparelli – On using verbs as nounsurn:uuid:9cbb23ce-a40d-36e9-957a-c4619e9b73562019-02-22T14:00:17.730600Z<div class="rubbullets rublinks text">
<p>Der Abstract für den Vortrag wird zwei bis drei Wochen vor dem Termin bekanntgegeben.</p>
</div>Radek Šimík – A situation-sensitive approach to bare NP interpretation: Evidence from Slavicurn:uuid:88423fa8-a754-3f6c-924a-3fce3e60cfd02019-02-22T14:00:17.734194Z<div class="rubbullets rublinks text">
<p>Der Abstract für den Vortrag wird zwei bis drei Wochen vor dem Termin bekanntgegeben.</p>
</div>Tristan Miller – Sense-based clustering for the interpretation of humorous ambiguityurn:uuid:99bfcceb-6f48-3a08-87dc-51379e3685572019-02-22T14:00:17.738001Z<div class="rubbullets rublinks text">
<p>Word sense disambiguation (WSD) – the task of determining which meaning
a word carries in a particular context – is a core research problem in
computational linguistics. Though it has long been recognized that
supervised approaches to WSD can yield impressive results, they require an
amount of manually annotated training data that is often too expensive or
impractical to obtain. This is a particular problem for processing the sort of
lexical-semantic anomalies employed for deliberate effect in humour and
wordplay. In contrast to supervised systems are knowledge-based techniques,
which rely only on pre-existing lexical-semantic resources (LSRs) such as
dictionaries and thesauri. In this talk, we treat the task of extending the
efficacy and applicability of knowledge-based WSD, both generally and for the
particular case of English puns. In the first part of the talk, we present two
approaches for bridging the “lexical gap” problem and thereby improving
WSD coverage and accuracy. In the first approach, we supplement the word’s
context and the LSR’s sense descriptions with entries from a distributional
thesaurus. The second approach enriches an LSR's sense information by
aligning and clustering the senses to those of other, complementary LSRs. In
the second part of the talk, we describe how these techniques, along with
evaluation methodologies from traditional WSD, can be adapted for the
“disambiguation” of puns, or rather for the automatic identification of their
double meanings.</p>
</div>Hana Filip – Two kinds of count nounsurn:uuid:1e4c2d3f-e0ba-3166-81d0-ced2fc4571ad2019-02-22T14:00:17.741927Z<div class="rubbullets rublinks text">
<p>There is a class of Ns that are grammatically count such as <em>fence</em>,
<em>wall</em>,
<em>twig</em>, that are non-quantized like bona fide singular count Ns (<em>cat</em>) but
also not cumulative like mass Ns (<em>mud</em>, <em>water</em>). Furthermore, puzzlingly,
many of these Ns are felicitous in pseudo-partitive, measure NPs: <em>Thick
woolen drapes of red and gold covered every inch of wall</em> (COCA). We
address this puzzle, which, to our knowledge, has so far not been
noticed in contemporary mass/count debates in formal semantics. We
argue that <em>fence</em>-like nouns admit of multiple individuation schemas
which leads to overlap with respect to what counts as one in their
denotations. As a result: (i) <em>fence</em>, like <em>cat</em>, but unlike <em>mud</em> is
quantized at specific counting contexts (and so is grammatically count),
(ii) <em>fence</em>, like <em>mud</em>, but unlike <em>cat</em> is non-quantized at the null
counting context which make them felicitous in measure NPs.</p>
</div>Paolo Acquaviva – Internalist semantics and the grammatical construction of individualsurn:uuid:e1cc2897-b2a2-38da-95c2-6ff71025a7442019-02-22T14:00:17.745544Z<div class="rubbullets rublinks text">
<p>Our use of language presupposes a domain of entities, but this domain
is at least in part a result of a conceptualization encoded in language.
How to analyze linguistic conceptualization without falling into a
simplistic Sapir-Whorf relativism? I address this challenge by
distinguishing a basic domain of abstract entities, each named by a
noun, from the domain of discourse referents, denoted by DPs. In
between, grammar provides a template organizing part-structural
information in different ways across languages. This explains a cluster
of phenomena relative to kind-interpretation, number, and countability,
unifies the analysis of nouns with that of names, and makes possible a
predictive theory of possible nouns in natural language. In this way,
lexical semantics can be integrated with a “grounded” approach to
cognition, as the form for representing the substance provided by the
mental recreation of experience.</p>
</div>Guillaume Thierry – Is language under control or in control?urn:uuid:0a50833b-de38-35ca-9829-b0d5c08e21612019-02-22T14:00:17.749201Z<div class="rubbullets rublinks text">
<p>In this talk, I will show how bilingual adults spontaneously access native
translations of second language words unconsciously and unknowingly stop
accessing these representations when second language words are unpleasant.
Even more surprising, bilinguals unconsciously access the sound of words in
their native language while speaking in their second. More surprising still,
cross-language effects extend to the domain of syntax: Welsh-English
bilinguals spontaneously transfer to English a morpho-phonological
transformation rule of Welsh that is entirely alien to their native language!
And perhaps worryingly, bilinguals engaging in a gambling task for money
take more risk when receiving verbal feedback in their native as compared to
their second language. Taken together these findings reveal unsuspected
levels of automaticity and cognitive diversity linked to language variations
within and between individuals. This realization calls for a reconsideration of
the way in which we conceptualise free will and operations classically
regarded as volitional.</p>
</div>Ana Marasović – A mention-ranking model for abstract anaphora resolutionurn:uuid:8ee9fb6e-7cbc-37ec-b54a-aad95b4dca372019-02-22T14:00:17.752896Z<div class="rubbullets rublinks text">
<p>Abstract anaphora resolution (AAR) is a challenging task that aims to resolve
anaphoric reference of pronominal and nominal expressions that refer to
abstract objects like facts, events, propositions, actions or situations, in the
(typically) preceding discourse. A central property of abstract anaphora is that
it establishes a relation between the anaphor embedded in the anaphoric
sentence and its (typically non-nominal) antecedent. In this talk, I will present
a mention-ranking model that learns how abstract anaphors relate to their
antecedents with an LSTM-Siamese Net. I will describe how we harvested
training data from a parsed corpus using a common syntactic pattern
consisting of a verb with an embedded sentential argument. I will show
results of the mention-ranking model trained for shell noun resolution and
results on an abstract anaphora subset of the ARRAU corpus. The latter
corpus presents a greater challenge due to a mixture of nominal and
pronominal anaphors and a greater range of confounders. Finally, I will talk
about ideas on how the training data extraction method and the mention-
ranking model could be further improved for the challenges ahead.</p>
</div>Heike Adel – Deep learning methods for knowledge base populationurn:uuid:2553c2c5-ed56-36fc-bf71-44b59eea203b2019-02-22T14:00:17.756555Z<div class="rubbullets rublinks text">
<p>Knowledge bases store structured information about entities or concepts of
the world and can be used in various applications, such as information
retrieval or question answering. A major drawback of existing knowledge
bases is their incompleteness. In this talk, I will present deep learning
methods for automatically populating them from text, addressing the
following tasks: slot filling, type-aware relation extraction and uncertainty
detection. Slot filling aims at extracting information about entities from a
large text corpus. I will present the system we developed for this task and
especially focus on its classification module for which we designed
contextCNNs, convolutional neural networks based on context splitting. In
the second part of the talk, I will present our investigations of type-aware
relation extraction with neural networks and introduce novel models for joint
entity and relation classification: a jointly trained model and a globally
normalized model. The last part of the talk focuses on assessing the factuality
of statements extracted from text. I will introduce external attention, a novel
attention variant which can incorporate external knowledge sources. To the
best of our knowledge, we are the first to integrate an uncertainty detection
component into a slot filling pipeline, extending the applicability of the
system to another use case.</p>
</div>Vibeke Rønneberg – Cognitive predictors of shallow-orthography spellingurn:uuid:858a8e17-d033-39dc-9db7-e956ed8a25892019-02-22T14:00:17.760219Z<div class="rubbullets rublinks text">
<p>This study investigates spelling behavior in a sample of 121 Norwegian 6th
graders completing a standardized spelling-to-dictation task. Students spelled
on computers with accurate recordings of initial onset latency and keystroke
latencies (the time interval between pressing keys). Measures of keyboard
familiarity, typing speed, short term memory, RAN, word reading, and non-
verbal cognitive skills were also collected. The present research had two aims.</p>
<p>First, we wanted to investigate what student-level cognitive factors affect
spelling performance, both spelling accuracy and the spelling process.</p>
<p>Second we aimed to develop understanding of time course of orthographic
planning in Norwegian, a language with a relatively shallow orthography, and
specifically the extent to which orthographic planning is complete before
typing onset. Results suggest that the activation of the orthographic
representation of a word is not complete when the children start to spell.</p>
</div>Artemis Alexiadou – The many faces of pluralurn:uuid:54e52eb1-2929-3fd2-9f6a-1992e87ce67b2019-02-22T14:00:17.763837Z<div class="rubbullets rublinks text">
<p>The category Number and in particular plural Number has received a lot of attention in the literature. In this talk, I will re-visit the question of whether plurals behave alike across languages. The focus will be on languages that have distinct Number systems (i.e. they differ in terms of the interpretation of the plural and the availability of general number).</p>
<p>I will introduce two distinct views on plurality and make particular assumptions about its morpho-syntactic representation. I will then discuss phenomena that show the non-uniform behavior of plurals across languages; these relate to semantic vs. morphological markedness. I will evaluate various theoretical approaches to plurality partially on the basis of experimental data. Finally, I will examine how the morpho-syntactic analysis of plurality can be used to understand the role of plural in nominalizations.</p>
<p>The empirical domain will include data from English, German, Greek, Hungarian, and Turkish.</p>
</div>Detmar Meurers – Analyzing learner language: Conceptual challenges and opportunities for computational linguisticsurn:uuid:3143dba7-47d9-3545-81f9-59de134e7f6d2019-02-22T14:00:17.767493Z<div class="rubbullets rublinks text">
<p>Learner corpora as collections of language produced by second language learners have been systematically collected since the 90s, and with computer-based language learning environments the opportunities for collecting data about the process and product of learning are substantially increasing. This offers a growing empirical basis on which theories of second language acquisition and interlanguage systems can be informed (cf. Meurers & Dickinson, 2017).</p>
<p>Yet, as soon as the research questions go beyond the acquisition of vocabulary and constructions with unambiguous surface indicators, corpora must be enhanced with linguistic annotation to support efficient retrieval of the data that is relevant for such research questions. In contrast to the different types of linguistic annotation schemes which have been developed for native language corpora, the discussion on which linguistic analysis and annotation is meaningful and appropriate for learner language is only starting.</p>
<p>When formulating linguistic generalizations, one generally relies on a long tradition of linguistic analysis that has established an inventory of categories and properties to abstract away from the surface strings. In this talk, we will see that traditional linguistic categories are not necessarily an appropriate index into the space of interlanguage realizations and their systematicity, which research into second language acquisition aims to capture.
Complementing the language explicitly given in the corpus, we also consider the need for information about the tasks (i.e., the functional context) that resulted in the texts collected in the corpus in order to annotate and support valid interpretations of the data.</p>
</div>Paramita Mirza – Mining temporal and causal relations from texturn:uuid:163b551d-802c-3d09-93fd-437b381aa7392019-02-22T14:00:17.771464Z<div class="rubbullets rublinks text">
<p>Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about events.</p>
<p>In this talk, automatic extraction of temporal information from texts will be discussed, with more emphasis on temporal ordering and causal relations, and their interaction based on the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve temporal/causal relation extraction systems will also be discussed, including word embeddings and common sense knowledge.</p>
</div>Grzegorz Chrupala – Representations of language in visually grounded neural modelsurn:uuid:4868734e-bd47-3720-a012-7a37ccccaee32019-02-22T14:00:17.775141Z<div class="rubbullets rublinks text">
<p>The task of learning language in a visually-grounded setting, with weak and noisy supervision, is of interest to scientists trying to understand the human mind as well as to engineers trying to build smart conversational agents or robots. In this talk I present models of grounded language learning based on recurrent neural networks which learn language from sentences paired with images of corresponding visual scenes. Input sentences are given at different levels of granularity: as sequences of words, sequences of phonemes, or as an acoustic signal.</p>
<p>I evaluate the internal representations induced by the models in these scenarios and present quantitative and qualitative analyses of their characteristics. I show how they encode language form and function, and specifically how they are selectively sensitive to certain aspects of language structure such as word boundaries, lexical categories and grammatical functions.</p>
</div>Sabine Schulte im Walde – Distributional approaches to semantic relatednessurn:uuid:eba7511e-b9c6-3682-8253-3dd21146595e2019-02-22T14:00:17.778818Z<div class="rubbullets rublinks text">
<p>Distributional models assume that the contexts of a linguistic unit (such as a word, a multi-word expression, a phrase, a sentence, etc.) provide information about the meaning of the linguistic unit (Harris, 1954; Firth, 1957). They have been widely applied in data-intensive lexical semantics (among other areas), and proven successful in diverse research issues, such as the representation and disambiguation of word senses; selectional preference modelling; the compositionality of compounds and phrases, or as a general framework across semantic tasks.</p>
<p>While it is clear that distributional knowledge does not cover all the cognitive knowledge humans possess with respect to word meaning (Marconi, 1997; Lenci, 2008), distributional models are very attractive, as the underlying parameters are accessible from even low-level annotated corpus data. We are thus interested in maximizing the benefit of distributional information for lexical semantics, by exploring the meaning and the potential of comparatively simple distributional models.</p>
<p>In this respect, this talk will present four case studies on semantic relatedness tasks that demonstrate the potential and the limits of distributional models: (i) the availability of various German association norms in standard web and newspaper corpora; (ii) the prediction of compositionality for German multi-word expressions; (iii) the distinction between the paradigmatic relations synonymy, antonymy and hypernymy with regard to German nouns, verbs and adjectives; and (iv) the integration and evaluation of distributional semantic information into an SMT system.</p>
</div>Maria Rauschenberger – Detection and intervention of dyslexiaurn:uuid:63dca8a8-d395-3c8c-b10a-55b2ca0f92872019-02-22T14:00:17.782532Z<div class="rubbullets rublinks text">
<p>Dyslexia is a specific reading disorder, which is probably caused by the “phonological skills deficiencies associated with phonological coding deficits” (Vellutino, Fletcher, Snowling, & Scanlon, 2004). A person with dyslexia has visual and auditory difficulties that cause problems in reading and writing (Deutsches Institut für Medizinische Dokumentation und Information, 2008). Dyslexia is frequent: worldwide, about 10% of the population and from 5 to 12% of the German students have dyslexia.</p>
<p>The problem is that children with dyslexia can learn the spelling of words or decode words for reading but they need more time to practice. For example, two years instead of one for learning how to spell phonetically accurate words (Schulte-Körne, 2010). Therefore we will first show the power of misspellings for intervention and how a game can help children to decrease their spelling mistakes. We will present the annotation of the errors, the creation of the exercises, and the Dyseggxia application in German.</p>
<p>In contrast to the intervention with misspellings - linguistic features have also the power to predict the risk of dyslexia for children. Therefore the linguistic features which are implemented in the game Dytective will be presented.</p>
<p>Finally, we show the concept of a possible and different approach of “How to predict dyslexia without linguistic features”.</p>
</div>Heike Zinsmeister – Zur Funktion und Resolution von nicht-nominalen Anaphernurn:uuid:e55e83f5-23da-350e-8a13-fb0f0850b6422019-02-22T14:00:17.786256Z<div class="rubbullets rublinks text">
<p>Anaphern (sprachliche Wiederaufnahme von anderweitig im Text eingeführten Referenten) tragen maßgeblich dazu bei, dass ein Text als kohärente Einheit wahrgenommen wird. Wiederaufgreifende Elemente sind dabei typischerweise pronominale Formen oder definite Nominalphrasen. Eine besondere Form der Anapher liegt dann vor, wenn sich die Wiederaufnahme auf Ereignisse, Tatsachen oder Zustände bezieht, die im Text nicht durch Nominalphrasen, sondern durch Sätze oder andere nicht-nominale Strukturen etabliert wurden.</p>
<p>(1) Hunde nehmen Farben anders wahr als Menschen. Hersteller von Hundespielzeug nehmen <em>darauf</em> / auf <em>diese Tatsache</em> jedoch keine Rücksicht.</p>
<p>Nicht-nominale Anaphern stellen besondere Herausforderungen an die (auto-matische) Interpretation, da die Kandidatenmenge der möglichen Bezugselemente unübersichtlich ist und anders als bei Anaphern mit nominalen Antezedenten hier nicht auf Kasus, Genus oder ähnliche Filter zurückgegriffen werden kann.</p>
<p>Der Vortrag richtet sich sowohl an Linguist/innen als auch an Computerlinguist/innen und nähert sich dem Phänomen nicht-nominaler Anaphern von drei Seiten: (i) Besonderheiten der Interpretation, (ii) textlinguistische Funktionen und (iii) ein Ausblick auf aktuelle Ansätze der automatischen Resolution.</p>
</div>Jana Häussler – Experimentelle Syntax im Sprachvergleich: Parallelexperimente zu Superioritätseffekten in 7 Sprachenurn:uuid:51142666-4130-32d9-8251-f854683a15432019-02-22T14:00:17.789845Z<div class="rubbullets rublinks text">
<p>Die Bewegung eines <em>Wh</em>-Objekts vor ein <em>Wh</em>-Subjekt wie in <em>Was sagt wer?</em> führt typischerweise zu verminderter Akzeptabilität. Dies wird als Superioritätseffekt bezeichnet. Im Vortrag werde ich eine Studie vorstellen, die den Effekt in sieben Sprachen (Deutsch, Englisch, Isländisch, Niederländisch, Schwedisch, Spanisch, Tschechisch) mithilfe mehrerer, weitgehend paralleler Erhebungen von Akzeptabilitätsurteilen untersucht.</p>
<p>Die Ergebnisse zeigen, dass mehrere Faktoren zum Superioritätseffekt beitragen, wie etwa Verarbeitungsschwierigkeiten aufgrund von Cue-Konflikten und langen Abhängigkeiten sowie ein Malus für <em>Wh</em>-Subjekte in situ. Entscheidend jedoch ist die Verfügbarkeit overter Kasusmarkierungen.</p>
</div>Michael Franke – Complex probability expressions & higher-order uncertainty: Compositional semantics, probabilistic pragmatics, and experimental dataurn:uuid:219f7348-89f2-389f-9c5b-0d7915cefeb52019-02-22T14:00:17.793383Z<div class="rubbullets rublinks text">
<p>[joint work with Michele Herbstritt]: We present novel experimental data pertaining to the use and interpretation of simple probability expressions (such as “possible” or “likely”) and complex ones (such as “possibly likely”) in situations of higher-order uncertainty, i.e., where speakers may be uncertain about the probability of a chance event. The data is used to critically assess a probabilistic model of speaker production and listener comprehension. The model embeds a simple compositional threshold-semantics for probability expressions, following recent work in formal linguistics, and formalizes general Gricean reasoning on top of it, taking the speaker’s higher-order uncertain belief states into account.</p>
</div>Bert Cappelle – Short-circuiting: How constructions constrain what ‘can’ can mean in contexturn:uuid:b102d3d3-693a-34d0-affb-67b532926f042019-02-22T14:00:17.796845Z<div class="rubbullets rublinks text">
<p>Der Abstract für den Vortrag wird zwei bis drei Wochen vor dem Termin bekanntgegeben.</p>
</div>Anne Temme – Die besondere Natur von Experiencer-Objekt-Strukturenurn:uuid:fcfc311f-7c15-3375-8a2f-147a0bc352b02019-02-22T14:00:17.800324Z<div class="rubbullets rublinks text">
<p>Der Abstract für den Vortrag wird zwei bis drei Wochen vor dem Termin bekanntgegeben.</p>
</div>James Henderson – Learning non-parametric vectorial representations for semantic entailmenturn:uuid:06f1bf06-1392-3d50-b44c-58f67fcb20552019-02-22T14:00:17.803899Z<div class="rubbullets rublinks text">
<p>Distributional semantics uses the distribution of words in their contexts
in text to induce vector-space representations of words (word
embeddings). It has been very successful at capturing semantic
similarity between words. But previous work has had difficulty using
these representations to capture lexical entailment, such as hyponymy,
which is an asymmetric relation measuring information inclusion.</p>
<p>This talk presents distributional semantic models where the induced
vector representations are designed to capture entailment. These models
use the recently proposed entailment-vectors framework to model the
semantic relationship between a word and its context. Training these
models on a large corpus of text induces entailment-vector
representations of words, which indicate when one word’s vector entails
another word’s vector. We evaluate these entailment predictions on the
task of hyponymy detection, achieving a substantial improvement over
previous results.</p>
</div>Vivi Nastase – Investigations in knowledge graphsurn:uuid:7cc7d944-f797-3716-99c1-939c6feeaeac2019-02-22T14:00:17.807503Z<div class="rubbullets rublinks text">
<p>Knowledge graphs are useful and flexible knowledge representation
structures that can facilitate the integration of information in NLP tasks.
They are however incomplete, and not only that, but also skewed in the
type of knowledge they include. In this talk I will present an
investigation into two existing knowledge graphs – <em>Freebase15k</em> and
<em>WordNet18</em> – and show how particular characteristics influence the
quality of knowledge graph embeddings, which ultimately impact
knowledge graph completion and other tasks. I will also talk about
knowledge discovery in knowledge graphs – as paths associated with
direct relations – and how these patterns can be used for both "internal"
knowledge graph completion and targeted information extraction from
external textual sources.</p>
</div>Manfred Stede – The Potsdam argumentative microtext corpus: Classification experiments and new extensionsurn:uuid:1af117af-eb94-35da-9abe-02aa5a12bb642019-02-22T14:00:17.811084Z<div class="rubbullets rublinks text">
<p>Argumentative “microtexts” are short texts produced by students in response
to a trigger question that invites taking a stance and providing arguments in
support of that stance. Our original corpus of 115 texts of this kind, collected
in a classroom setting, was published in 2015, together with annotations of
the underlying argumentation structure. In the talk, I first describe our
experiments on automatically classifying those structures, which make use of
the minimum-spanning tree algorithm, and of Integer Linear Programming.
Then I turn to various extensions made to the corpus just recently: New
annotation layers that allow for computing correlations with properties of
argumentation structure, as well as an extension of the overall data set by 200
new texts that were obtained via crowdsourcing. I will summarize our
experiences with this approach to text production and explain the extra steps
needed to make the crowdsourced data compatible to the original corpus.</p>
</div>Lucas Champollion – Donkeys without bordersurn:uuid:39deac9e-7f38-3939-b4e8-d6398e7f8d072019-02-22T14:00:17.814885Z<div class="rubbullets rublinks text">
<p>Donkey sentences have existential and universal readings, but they are not
often perceived as ambiguous. We extend the pragmatic theory of non-
maximality in plural definites by Križ (2016) to explain how hearers use
Questions under Discussion to fix the interpretation of donkey sentences
in context. We propose that the denotations of such sentences involve truth-
value gaps – in certain scenarios the sentences are neither true nor false –
and demonstrate that Križ’s pragmatic theory fills these gaps to generate
the standard judgments of the literature. Building on Muskens’s (1996)
Compositional Discourse Representation Theory and on ideas from
supervaluation semantics, we define a general schema for dynamic
quantification that delivers the required truth-value gaps.
(This talk presents joint work with Dylan Bumford and Robert Henderson.
Paper at <a href="http://ling.auf.net/lingbuzz/003333">http://ling.auf.net/lingbuzz/003333</a>)</p>
</div>Dirk Hovy – Texts come from people: Computational sociolinguistics and NLPurn:uuid:4544f7be-d801-3568-86e3-9b68ad7312b32019-02-22T14:00:17.818553Z<div class="rubbullets rublinks text">
<p>In this talk, I will show how we can combine statistical NLP methods and sociolinguistic theories to the benefit of both fields. I present research on large-scale statistical analysis of demographic language variation to detect factors that influence performance (and fairness) of NLP systems, and how we can incorporate demographic information into statistical models to address both problems.</p>
<p>Sociolinguistics has long investigated the interplay of demographic factors and language use, and the same factors are also present in the data we use to train Natural Language Processing (NLP) systems.</p>
<p>NLP models, however, are based on a small demographic sample and approach all language as uniform. As a result, NLP models perform worse for demographic groups that differ from the training data. This bias harms performance and can disadvantage entire user groups. I will show how adding demographic information to NLP models can improve performance and create fairer systems for everyone.</p>
</div>Berit Gehrke – ‘Good’ as an evaluative intensifierurn:uuid:337962ec-90f7-384a-bbea-607c15a08fb62019-02-22T14:00:17.822224Z<div class="rubbullets rublinks text">
<p>This talk aims to contribute to the ongoing debate on the semantics of evaluative modifiers from the perspective of the interaction between meaning types, and in view of two related Catalan modifiers, namely the ad-nominal <em>bon</em> ‘good’(e.g. <em>una bona estona</em> ‘a good while’) and the ad-adjectival <em>ben</em> ‘well’ (e.g. <em>ben clar</em> ‘well clear’). We build on the lexical semantics of the predicate good and the subsective composition mode to claim that good selects the good instances in the extension of N/A. This yields intensification whenever the extension is ordered. Moreover, a monotonicity inference is conveyed through a Conventional Implicature, which makes evaluative modifiers unacceptable under negation.</p>
</div>Peter Auer – Projektionen im Rahmen der Online-Syntaxurn:uuid:334fe9f7-4cc9-3065-9c40-26ca0a8c37702019-02-22T14:00:17.825929Z<div class="rubbullets rublinks text">
<p>Trotz des inszenierten medialen Rätselratens über die missglückte Twitter-Nachricht des amerikanischen Präsidenten: <em>Despite the constant negative press covfeve</em> war diese relativ eindeutig dekodierbar. Das lag einerseits daran, dass der Wortanfang <em>cov</em> Vorhersagen über das geplante Wort möglich macht und anderseits der Satzanfang in dem Slot nur eine begrenzte Anzahl von Weiterführungen nach <em>press</em> bis zum Abschluss des Konzessivsatzes erlaubt. In Kombination führt dies zur Interpretation 'coverage'.</p>
<p>Das Beispiel zeigt, dass die Rezeption von Sprache (selbst im schriftlichen, noch mehr aber im mündlichen Medium) inkrementell und „online“ erfolgt, d. h. in und mit der Produktionszeit. Ich sehe „Grammatik“ (u. a.) als ein Verfahren menschlicher Sprache an, um in diesem Prozess Projektionen über den weiteren Verlauf der emergenten Äußerung möglich zu machen. In meinem Vortrag werde ich zeigen, warum dies für die menschliche Kommunikation äußerst sinnvoll ist und außerdem darauf eingehen, wie verschiedene syntaktische Verfahren dem Ziel der Projektionsoptimierung mehr oder weniger gut dienen.</p>
</div>Lewis Bott – What structural priming tells us about implicaturesurn:uuid:3e0aa2ac-646b-3759-904d-8072820467b52019-02-22T14:00:17.829429Z<div class="rubbullets rublinks text">
<p>Utterances communicate much more than the literal meaning of the words. For instance, a letter of reference describing a student as punctual might imply that their academic ability is modest, or a grant application rated as good might suggest that the reviewer thought that it wasn’t excellent. These sorts of enrichments are commonly referred to as implicatures.</p>
<p>I will present experiments investigating the underlying representation and processing of implicatures and related phenomena. The approach I take is to test whether implications such as those above can be primed, that is, whether enrichment in preceding context leads to enrichment in the current context. The results help us to understand the psycholinguistics of Gricean implications and pragmatic processing more generally.</p>
</div>Patrick Rebuschat – Implicit-statistical learning of words and syntax: Evidence from cross-situational learningurn:uuid:caf0fe25-a6d3-333b-9191-442e8b9728462019-02-22T14:00:17.833010Z<div class="rubbullets rublinks text">
<p>In this talk, I will present recent experiments that bring together methodological insights from two related, yet completely distinct research strands, namely “implicit learning” (Reber, 1967) and “statistical learning” (e.g., Saffran et al, 1996). In the first part, I will discuss experiments that used verbal reports and subjective measures of awareness to determine what strategies subjects followed in the learning task and whether they became aware of rules or patterns. Results indicate that provision of prior (explicit) knowledge significantly boosts implicit-statistical learning. In the second part, I will introduce a novel artificial language paradigm that is part of a long-term project on individual differences in language learning across the lifespan. Our results demonstrate that adult learners can simultaneously acquire lexical and syntactic information by keeping track of cross-trial statistics, after brief exposure, without feedback and without the conscious intention to learn. We conclude by discussing implications for future research.</p>
</div>Ekaterina Lapshinova-Koltunski – Discourse analysis and annotation for contrastive linguistics and translation studiesurn:uuid:247ad731-1f03-3ea1-b5e6-87e44293887f2019-02-22T14:00:17.836591Z<div class="rubbullets rublinks text">
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<p>In this talk, I will report on the ongoing work on discourse analysis in a multilingual context. I will present two approaches in the analysis of coreference and discourse-related phenomena: (1) top-down or theory-driven: here we start from some linguistic knowledge derived from the existing frameworks, define linguistic categories to analyse and create an annotated corpus that can be used either for further linguistic analysis or as training data for NLP applications; (2) bottom-up or data-driven: in this case, we start from a set of features of shallow character that we believe are discourse-related. We extract these structures from a huge amount of data and analyse them from a linguistic point of view trying to describe and explain the observed phenomena from the point of view of existing theories and grammars.</p>
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